System for recognizing fingerprint image, method and program for the same

ABSTRACT

A fingerprint recognition system for extracting minutiae from a fingerprint image. The fingerprint recognition system generates a corrected image from the input fingerprint image by: eliminating incipient ridges/pores from the fingerprint image by using the density pattern of the pixels of ridge lines/valley lines in the direction orthogonal to the length direction of the ridge lines. The minutiae are extracted from the corrected image.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.11/500,467, filed Aug. 8, 2006, claiming priority based on JapanesePatent Application No. 2005-231150, filed Aug. 9, 2005, the contents ofall of which are incorporated herein by reference in their entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to the recognition of a fingerprint image.

2. Description of the Related Art

Conventionally, in a fingerprint matching, as a fingerprint matchingapparatus noted in Japanese Laid Open Patent Application (JP-A-Showa,59-778) or in its U.S. Pat. No. 4,646,352, a minutia matching has beenwidely used by using the end point and bifurcation of a fingerprintridge (either of them is referred to as a common word “minutia”).

In the conventional technique, it is difficult to accurately distinguishminutiae from incipient ridge lines and sweat pores. The “incipientridge line” means the growing line that does not become the ridgeperfectly.

As one of the conventional techniques for processing a low qualityfingerprint image containing a noise, Japanese Laid Open PatentApplication (JP-A-Heisei, 8-287255) or its U.S. Pat. No. 6,018,586discloses an apparatus and an image processor for extracting imagefeatures of a skin pattern.

The apparatus of this conventional technique includes an image featureextracting apparatus of a skin pattern image for extracting an imagefeature in a plurality of local regions set on an image in advance. Theimage feature extracting apparatus has an image memory, a group offilters memory, an image magnitude calculator and a feature calculator.

The image memory stores a skin pattern image. The group of filtersmemory stores a plurality of filters composed of a two-dimensional arrayof weighting factors to extract the image features in the local regions.The filtering means filters the skin pattern images stored in the imagememory by using the plurality of filters stored in the group of filtersmemory, respectively. The image magnitude calculator calculates theimage magnitudes in the plurality of local regions set on the images inadvance, for the respective images obtained by the filtering device. Thefeature calculator uses the information of the image magnitudes of thelocal regions which are obtained by the image magnitude calculator andthen calculates the image features in the respective local regions.

According to the method proposed in the Japanese Laid Open PatentApplication (JP-A-Heisei, 8-287255) or in its U.S. Pat. No. 6,018,586,the plurality of two-dimensional filters having the weighting factors,which correspond to the image features of ridge directions and ridgepitches are prepared in advance, and the filters are used to carry out afilter enhancement. Then, the result of the filtering process having themaximum image magnitude is selected, thereby executing the effectiveimage enhancement and also extracting the ridge direction and ridgepitch, which correspond to the selected filter, as the image feature.Consequently, the noise is removed.

Also, although the conventional techniques for removing the sweat poresfrom the fingerprint image have been variously proposed, most of themuse the supposition that the sweat pore is surrounded with ridge pixels.For example, Japanese Laid Open Patent Application (JP-A-Heisei,5-205035) discloses a fingerprint matching apparatus.

This fingerprint matching apparatus is provided with a fingerprintsensor, a fingerprint image memory, a gray-scale image corrector and amain body device.

The fingerprint sensor scans a fingerprint image. The fingerprint imagememory temporally stores a fingerprint image data read by thefingerprint sensor. The gray-scale image corrector calculates a contourline of the gray-scale image of the fingerprint from the fingerprintimage stored in the fingerprint image memory. Then, false minutiae, suchas an island, a bridge, a sweat pore or the like are extracted from theimage constituted by the contour lines, and if the minutia is the islandor bridge, the density of the portion corresponding to a closed curvesurrounded with those contour lines is decreased. On the other hand, ifthose minutiae are the sweat pores, the density of the portioncorresponding to the closed curve surrounded with those contour lines isincreased and the false minutiae are removed from the gray-scale image.

As another conventional technique, Japanese Laid Open Patent Application(JP-P2001-14464A) discloses an apparatus for fingerprint imageprocessing and the method for the same. This technique has a filteringmeans for eliminating sweat pores from a fingerprint image.

As still another conventional technique, Laid Open Patent Application(JP-P2001-243467A) discloses a sweat pore portion judging apparatus.This technique has an algorism for distinguishing sweat pores fromislands by using the direction of ridge line.

As yet still another conventional technique, Japanese Laid Open PatentApplication (JP-A-Heisei, 1-213757) discloses a sweat pore eliminatingprocessing apparatus.

SUMMARY OF THE INVENTION

In the conventional noise reduction technique disclosed in alreadymentioned Japanese Laid Open Patent Application (JP-A-Heisei, 8-287255)or its U.S. Pat. No. 6,018,586, when the incipient ridges or sweat poresare thick, there is a possibility that false ridges are extracted. Forexample, in an example of a fingerprint image where the incipient ridgelines and the sweat pores are thick as shown on the left side picture ofFIG. 12 and the left side picture of FIG. 21, many incipient ridge linesand the sweat pores exist. If the filter corresponding to the case wherethe ridge pitch is half of the ideal value is used to execute the filterenhancement, the image magnitude becomes maximum. In this case, as shownon the right side of FIG. 12 and the right side of FIG. 21, two ridgesare extracted from a region where only one ridge should be extracted.

The fingerprint comparing apparatus disclosed in already mentionedJapanese Laid Open Patent Application (JP-A-Heisei, 5-205035) proposesthe method of extracting the contour line where the density of thegray-scale image is used, and regarding the pixel surrounded with thecontour line having the shape of a loop as the sweat pore and thencorrecting the gray-scale image and consequently removing the sweatpore. In such a method, when there is the noise in the ridge pixelssurrounding the sweat pore and the sweat pore is not surrounded with theclosed curve, the sweat pore is not extracted.

A method that makes it possible to stably recognize and remove theincipient ridge line and the sweat pore even for the fingerprint imagewhere the incipient ridge lines and the sweat pores are thick isdesired.

It is therefore an object of the present invention to provide a system,a method and a program for recognizing a fingerprint image, which makeit possible to stably recognize and remove the incipient ridge line andthe sweat pore even for the fingerprint image where the incipient ridgelines and the sweat pores are thick.

To achieve the object, the fingerprint recognition system according tothe present invention generates a corrected image from the inputfingerprint image by: eliminating incipient ridges/pores from thefingerprint image by using the density pattern of the pixels of ridgelines/valley lines in the direction crossing to (or intersecting with)the longitudinal direction of the ridge lines. The minutiae areextracted from the corrected image.

More precisely, as one aspect of the present invention, a method forrecognizing a fingerprint image includes: extracting a longitudinaldirection of a ridge from the fingerprint image; classifying a pluralityof ridges into true ridges and incipient ridges based on an analysis ofan incipient feature pattern of the plurality of ridges in a directioncrossing to the longitudinal direction; and generating a corrected imagedata by approximating densities of pixels classified as the incipientridges close to a density of a valley of the fingerprint image. Theminutiae are extracted from the corrected image.

Preferably, the method according to the present invention furtherincludes: extracting an area extending in the longitudinal direction inwhich an occupancy rate of the true ridges is higher than apredetermined condition as a ridge area; and accentuating the ridgearea.

As an another aspect of the present invention, a method for recognizinga fingerprint image includes: extracting a longitudinal direction of aridge from the fingerprint image; classifying a plurality of valleysinto true valleys and pores based on an analysis of a pore featurepattern of the plurality of valleys in a direction crossing to thelongitudinal direction; and generating a corrected image data byapproximating densities of pixels classified into the pores close to adensity of a ridge of the fingerprint image.

Preferably, the method according to the present invention furtherincludes: extracting an area extending in the longitudinal direction inwhich an occupancy rate of the true valley is higher than apredetermined condition as a valley area; and accentuating the valleyarea.

The system for recognizing a fingerprint image according to the presentinvention includes units, each of the units carries out the operationalsteps included in the method according to the present invention.

The computer readable software product for executing a recognition of afingerprint image according to the present invention is read by acomputer, and when executed, it causes the computer to execute themethod according to the present invention.

By executing the system, method, or the program for recognizing thefingerprint image according to the present invention, it is possible torealize at least one of the following effects.

As the first effect, for the fingerprint image having the thickincipient ridge lines, the method of extracting the incipient ridgefeatures, analyzing the properties of some incipient ridges in thedirection orthogonal to the ridge lines and judging the pattern of theincipient ridge features which are alternately changed between the trueridge lines and the incipient ridge lines in accordance with thepredetermined conditional equation makes it possible to accuratelyextract and remove the incipient ridge lines.

As the second effect, for the fingerprint image having the thick sweatpores, the method of extracting the sweat pore features and analyzingthe sweat pore features of some valley lines in the direction orthogonalto the ridge line and then judging the pattern of the sweat porefeatures which are alternately changed between the true valley lines andthe sweat pores in accordance with the predetermined conditionalequation makes it possible to accurately extract and remove the sweatpores.

As the third effect, for the fingerprint image without any thickincipient ridge line, the method of extracting the incipient ridgefeatures and analyzing the incipient ridge features of some ridges inthe direction orthogonal to the ridge and then judging that there is nosignificant difference between all of the incipient ridge features inaccordance with the predetermined conditional equation makes it possibleto accurately extract and enhance the ridges.

As the fourth effect, for the fingerprint image without any thick sweatpore, the method of extracting the sweat pore features, and analyzingthe sweat pore features of some valley lines in the direction orthogonalto the ridge and then judging that there is no significant differencebetween all of the sweat pore features in accordance with thepredetermined conditional equation makes it possible to accuratelyextract and enhance the valley lines.

As the fifth effect, by using the ridge width as the incipient ridgefeature, the incipient ridge lines can be accurately extracted.

As the sixth effect, by using the ridge direction density as theincipient ridge feature, the incipient ridge lines can be accuratelyextracted.

As the seventh effect, by using the ridge direction density as the sweatpore feature, the sweat pores can be accurately extracted.

As the eighth effect, by using the accumulated difference of the densityas the sweat pore feature, the sweat pore can be accurately extracted.

As the ninth effect, by using the white pixel radius as the sweat porefeature, the sweat pore can be accurately extracted.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an overall view of the embodiment ofthe present invention;

FIG. 2 is a configuration view of an incipient ridge line extracting andeliminating unit;

FIG. 3 is a configuration view of a sweat pore extracting andeliminating unit;

FIG. 4 is a configuration view of a ridge extracting and accentuatingunit;

FIG. 5 is an overall flowchart;

FIG. 6 is a flowchart of an incipient ridge line extracting and removingprocess;

FIG. 7 is a view showing a fingerprint image;

FIG. 8 is a view showing a false minutia;

FIG. 9 is a view showing a ridge direction;

FIG. 10 is a view showing a direction pattern (16 directions);

FIG. 11 is a view showing an example of a ridge enhancing mask;

FIG. 12 is a view showing a gray-scale image including an incipientridge line and a binary image;

FIG. 13 is a view showing the gray-scale image including the incipientridge line and a skeleton image;

FIG. 14 is a view showing a focused area after a rotation;

FIG. 15 is a view showing an example of an incipient ridge feature(ridge width);

FIG. 16 is a view showing an incipient ridge area;

FIG. 17 is a view showing an incipient ridge area density correctedimage;

FIG. 18 is a flowchart of a sweat pore extracting and removing process;

FIG. 19 is a view showing a fingerprint image;

FIG. 20 is a view showing an example of a false minutia;

FIG. 21 is a view showing a gray-scale image including a continuoussweat pore and its skeleton image;

FIG. 22 is a view showing a ridge including a sweat pore and a valleycandidate pixel;

FIG. 23 is a view showing a reversed density image of the ridgeincluding the sweat pore;

FIG. 24 is a view showing the ridge including the sweat pore and avalley candidate pixel;

FIG. 25 is a view showing an example of a sweat pore feature;

FIG. 26 is a view showing a sweat pore area;

FIG. 27 is a view showing a sweat pore area density corrected image;

FIG. 28 is a flowchart of a ridge extracting and eliminating process;

FIG. 29 is a view showing a fingerprint where enlarged pores are thick;

FIG. 30 is an enlarged view of enlarged pores and a view showing avalley candidate pixel;

FIG. 31 is a view showing a maximum density pixel group;

FIG. 32 is a view showing a sweat pore area; and

FIG. 33 is a view showing a sweat pore area density corrected pixel.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

A first embodiment of the present invention will be described below withreference to the attached drawings.

As shown in FIG. 1, the overall configuration of the present firstembodiment is provided with a fingerprint image inputting unit 11, aridge direction extracting unit 12, an incipient ridge extracting andeliminating unit 13, a pore extracting and eliminating unit 14, a ridgeextracting and accentuating unit 15, a minutia extracting unit 16 and aminutia outputting unit 17.

The fingerprint image inputting unit 11 inputs and digitizes afingerprint image scanned by a fingerprint sensor or scanner. The ridgedirection extracting unit 12 extracts the direction of a ridge line fromthe fingerprint image inputted by the fingerprint image inputting unit11. The incipient ridge extracting and eliminating unit 13 extracts andeliminates an incipient ridge line from the fingerprint image inputtedfrom the fingerprint image inputting unit 11 by using the direction dataextracted by the ridge direction extracting unit 12. The pore extractingand eliminating unit 14 extracts and eliminates a sweat pore from thefingerprint image inputted from the fingerprint image inputting unit 11by using the direction data extracted by the ridge direction extractingunit 12. The ridge extracting and accentuating unit 15 extracts theridge and valley from the fingerprint image inputted from thefingerprint image inputting unit by using the ridge direction dataextracted by the ridge direction extracting unit 12 and enhances thedensities of the ridge and the valley. The minutia extracting unit 16extracts the minutiae from the fingerprint image where the incipientridge lines are removed by the incipient ridge extracting andeliminating unit 13, the sweat pores are removed by the pore extractingand eliminating unit 14, and the ridge and valley line densities areenhanced by the ridge extracting and accentuating unit 15. The minutiaoutputting unit 17 outputs the minutia data extracted by the minutiaextracting unit 17.

The first feature of this embodiment lies in the configuration of theincipient ridge extracting and eliminating unit 13 as shown in FIG. 2.

In FIG. 2, the incipient ridge extracting and eliminating unit 13 (alsocalled as “data processing unit 13”) contains a data processing andcontrolling unit 21, a data storing unit 22, a ridge binary imageextracting unit 23, a ridge candidate pixel extracting unit 24, anincipient ridge feature extracting unit 25, an incipient ridge featureanalyzing unit 26 (which is called also as a first false featureanalyzing unit), an incipient ridge area extracting unit 27 and anincipient ridge area density correcting unit 28 and also has aninterface with the minutia extracting unit 16.

The data processing and controlling unit 21 carries out the overalloperation control of the incipient ridge extracting and eliminating unit13. The data storing unit 22 stores the various data obtained in therespective units in the incipient ridge extracting and eliminating unit13. The ridge binary image extracting unit 23 extracts a binary image inwhich a ridge portion is represented by “1” and a valley portion by “0”.The ridge candidate pixel extracting unit 24 converts the ridge binaryimage into a skeleton and consequently extracts a group of pixels whichis treated as the candidate of a ridge line. The incipient ridge featureextracting unit 25 extracts the incipient ridge feature for each pixelof the ridge candidate pixel group. The incipient ridge featureanalyzing unit 26 (which is called also as “first false featureanalyzing unit”. Here, the “false feature” means the incipient ridge,which should be eliminated in extracting minutiae) analyzes theincipient ridge feature extracted for the ridge candidate pixel group.The incipient ridge area extracting unit 27 analyzes the ridge candidatepixel group to which a true-ridge mark or incipient-ridge-line mark isassigned and extracts the incipient ridge area. The incipient ridge areadensity correcting unit 28 corrects the density value for the pixelgroup, which is defined as the incipient ridge area, and consequentlyextracts the image in which the incipient ridge line is eliminated.

The second feature of this embodiment lies in the configuration of thepore extracting and eliminating unit 14 as shown in FIG. 3.

In FIG. 3, the pore extracting and eliminating unit 14 (also called as“data processing unit”) contains a data processing and controlling unit31, a data storing unit 32, a valley binary image extracting unit 33, avalley candidate pixel extracting unit 34, a pore feature extractingunit 35, a pore feature analyzing unit 36 (which is called also as asecond false feature analyzing unit), a pore area extracting unit 37 anda pore area density correcting unit 38 and also has the interface withthe minutia extracting unit 16.

The data processing and controlling unit 31 carries out the overalloperation control of the pore extracting and eliminating unit 14. Thedata storing unit 32 stores the various data obtained in the respectiveunits in the pore extracting and eliminating unit 14. The valley binaryimage extracting unit 33 extracts a binary image in which a valleyportion is represented by “1” and a ridge portion by “0”. The valleycandidate pixel extracting unit 34 converts the valley line binary imageinto a skeleton and consequently extracts a group of pixels which istreated as the candidate of a valley line. The pore feature extractingunit 35 extracts the sweat pore feature for each pixel in the valleycandidate pixel group. The pore feature analyzing unit 36 (which iscalled also as “second false feature analyzing unit”. Here, the “falsefeature” means the sweat pore, which should be eliminated in extractingminutiae) analyzes the sweat pore feature extracted for the valleycandidate pixel group. The pore area extracting unit 37 analyzes thevalley candidate pixel group to which a true-valley mark or sweat-poremark is assigned and extracts the sweat pore area. The pore area densitycorrecting unit 38 corrects the density value for the pixel group, whichis defined as the sweat pore area, and consequently extracts the imagein which the sweat pore is eliminated.

The third feature of this embodiment lies in the configuration of theridge extracting and accentuating unit 15 as shown in FIG. 4.

In FIG. 4, the ridge extracting and accentuating unit 15 (also called as“data processing unit”) contains a data processing and controlling unit41, a data storing unit 42, a ridge binary image extracting unit 43, aridge candidate pixel extracting unit 44, an incipient ridge featureextracting unit 45, an incipient ridge feature analyzing unit 46, aridge area extracting unit 47 (which is called also as a third falsefeature analyzing unit), a ridge area density correcting unit 48, avalley binary image extracting unit 49, a valley candidate pixelextracting unit 50, a pore feature extracting unit 51, a pore featureanalyzing unit 52, a valley area extracting unit 53 (which is calledalso as a fourth false feature analyzing unit) and a valley area densityaccentuating unit 54 and also has the interface with the minutiaextracting unit 16.

The data processing and controlling unit 41 carries out the overalloperation control of the ridge extracting and accentuating unit 15. Thedata storing unit 42 stores the various data obtained in the respectiveunits in the ridge extracting and accentuating unit 15. The ridge binaryimage extracting unit 43 extracts a binary image in which a ridgeportion is represented by “1” and the valley portion by “0”. The ridgecandidate pixel extracting unit 44 converts the ridge binary image intoa skeleton and consequently extracts a group of pixels which is treatedas the candidate of a ridge line. The incipient ridge feature extractingunit 45 extracts the incipient ridge feature for each pixel in the ridgecandidate pixel group. The incipient ridge feature analyzing unit 46analyzes the incipient ridge feature extracted for the ridge candidatepixel group. The ridge area extracting unit 47 analyzes the ridgecandidate pixel group to which a true-ridge mark is assigned andextracts the ridge area. The ridge area density emphasizing unit 48corrects a density value for the pixel group, which is defined as theridge area, and consequently extracts the image where the ridge and thevalley line are enhanced. The valley binary image extracting unit 49extracts a binary image in which a valley portion is represented by “1”and the ridge portion by “0”. The valley candidate pixel extracting unit50 converts the valley line binary image into a skeleton andconsequently extracts a group of pixels which is treated as thecandidate of a valley line. The pore feature extracting unit 51 extractsthe sweat pore feature for each pixel in the valley candidate pixelgroup. The pore feature analyzing unit 52 analyzes the sweat porefeature extracted for the valley candidate pixel group. The valley areaextracting unit 53 analyzes the valley candidate pixel group to whichthe true-valley mark is assigned and extracts the valley area. Thevalley area density accentuating unit 54 corrects the density value forthe pixel group defined as the valley line area and consequentlyextracts the image where the ridge and the valley are enhanced.

The overall operations of this embodiment will be described below indetail with reference to the configuration view of FIG. 1, the entireflowchart of FIG. 5 and the related drawings in FIGS. 7 to 10.

(1) Step S1

At the step S1 in FIG. 5, the fingerprint image inputting unit 11 inputsa fingerprint image. The fingerprint image inputting unit 11 scanned anddigitized the fingerprint image or inputs the fingerprint file in whichalready digitized fingerprint data is included.

FIG. 7 is an example of the fingerprint image scanned by a sensor orscanner and digitized. The fingerprint image shown in FIG. 7 isdigitized so as to have the 256 gray-scales (from 0 to 255) at aresolution of 500 dpi (dot per inch), in accordance withANSI/NIST-CSL-1-1993 Data Format for the Interchange of Fingerprint,Facial & SMT Information, which was standardized on the basis of USNational Institute of Standards and Technology.

In the standard, the density value representation is defined inaccordance with a brightness standard where as the brightness becomesgreater (namely, brighter), the value becomes greater. However, in thepresent invention, the density value representation is explained underthe standard of density where, as the density becomes higher, the valuebecomes greater. Thus, the ridge portion which is high in density havethe value close to the maximum of 255, and the white portion of thebackground paper and ridge groove which are thin in density have thedensity value close to 0. Here, the ridge groove is similar to thevalley line, and indicates the relatively white portion between theridges.

(2) Step S2

Next, at the step S2, the ridge direction extracting unit 12 extractsthe direction of the ridge for each predetermined small region. Theridge direction (namely, longitudinal direction of the ridge line) ofthe fingerprint can be automatically extracted by applying, for example,the conventional technique disclosed in Japanese Laid Open PatentApplication (JP-A-Showa, 52-97298) or the conventional techniquedisclosed in Japanese Laid Open Patent Application (JP-P 2002-288641A).

FIG. 9 shows the result after the direction of the fingerprint image inFIG. 7 is extracted by the conventional technique disclosed in JapaneseLaid Open Patent Application (JP-P2002-288641A). In FIG. 9, short linesare overlapped and displayed on the fingerprint image. The short linesindicate the directions of the pieces of ridge lines each of which isplaced at a small region of 8×8 pixels. The ridge line directions arequantified into 16 discrete values as shown in FIG. 10.

The following incipient ridge line extracting and eliminating process,sweat pore extracting and eliminating process and ridge extracting andaccentuating process are performed on only the region where the ridgedirection is successfully extracted.

(3) Step S3

Next, at the step S3, the incipient ridge extracting and eliminatingunit 13 extracts and eliminates the incipient ridge line. This processis one of the main processes of this embodiment, and its detailedexplanation will be described later.

(4) Step S4

Next, at the step S4, the pore extracting and eliminating unit 14extracts and eliminates the sweat pores. This process is one of the mainprocesses of this embodiment, and its detailed explanation will bedescribed later.

(5) Step S5

Next, at the step S5, the ridge extracting and accentuating unit 15 inFIG. 1 extracts the ridge and the valley and enhances the densities ofthe ridge and the valley. This process is one of the main processes ofthis embodiment, and its detailed explanation will be described later.

The processes of the steps S3 to S5 can be executed in any order. Also,only any one process or any combination of two processes selected fromthe three processes S3 to S5 may be executed.

(6) Step S6

Next, at the step S6, the minutia extracting unit 16 in FIG. 1 extractsthe minutia of the fingerprint, such as an end point and a bifurcation,from the gray-scale image data. This minutia extracting process can berealized by applying the conventional techniques, for example, [PatternFeature Extracting Apparatus] of Japanese Laid Open Patent Application(JP-A-Showa, 55-138174) and its U.S. Pat. No. 4,310,827 “Device forextracting a density as one of pattern features for each minutia of astreaked pattern”.

(7) Step S7

Next, at the step S7, the minutia outputting unit 17 in FIG. 1 outputsthe minutia data extracted by the minutia extracting unit 16 to thesubsequent processing units. In the subsequent process, usually, in acase that an input image is on a filing side (for the purpose ofregistration), it is registered in a database, and in a case that theinput image is a searching side (for the purpose of inquiry), it is usedfor a minutia matching.

The operation of the incipient ridge extracting and eliminating unit 13will be described below in detail with reference to the configurationview in FIG. 2, the flowchart in FIG. 6 and the related views in FIGS. 7to 17.

FIG. 7 is an example of the fingerprint image where the incipient ridgelines are thick, and this image is used for explaining the incipientridge line extracting and eliminating process. If the incipient ridgelines are thick, the incipient ridge lines are erroneously judged as thetrue ridges. This misjudgment causes the emergence of a large number offalse minutiae. FIG. 8 shows an example of such false minutiae.

At a time when the incipient ridge line extracting and eliminatingprocess is started, the already-inputted fingerprint image and thealready-extracted ridge direction data of inputted fingerprint image arestored in the data storing unit 22 in FIG. 2.

(1) Step S31

At the step S31 in FIG. 6, the ridge binary image extracting unit 23shown in FIG. 2 extracts the binary image in which the ridge portion isrepresented by “1” and the ridge groove by “0”. In the binary image, the“1” of the ridge portion is also referred to as a black pixel, and the“0” of the ridge groove is also referred to as a white pixel.

In order to reduce the influence of the noise in extracting thefingerprint image ridge, typically, a smoothing process is performed onthe ridge direction, and an edge enhancement process is performed on adirection orthogonal to the ridge. Such a process can be carried out byusing the conventional technique disclosed in [Stripe Pattern ExtractingApparatus] of Japanese Laid Open Patent Application (JP-A-Showa,51-77138). In this embodiment, a ridge enhancing mask shown in FIG. 11is used to enhance the ridge and reduce the noise. In this maskingprocess, a mask is selected corresponding to the ridge direction of aremarked pixel, the remarked pixel is set to the center of the mask, thedensities of vicinity pixels are multiplied by the weighting factors ofthe mask, and the summation of the multiplied values is used as thedensity of remarked pixel.

Next, a ridge density threshold is calculated for the binary imageconversion. Various threshold calculating methods for converting thefingerprint gray-scale image into the binary value have been proposed.Here, one example that enables the easy calculation is indicated.

A density distribution of pixels within a 10-pixel radius with a certainremarked pixel as a center is investigated. This length of the radius(in this example, 10 pixels) is defined as the distance approximatelyequal to an average width between the neighboring ridges so that bothfingerprint ridge and valley are contained within an inspection range.Here, the line generated in the groove between the two ridges adjacentto each other is referred to as the valley line.

In a conventional technique, the middle value between the maximum valueand the minimum value in the nearby density distribution is used as thethreshold. For reducing the noise possibly included in the pixels otherthan the ridge portion and the valley portion, it is desirable toexclude the pixel having the densest density value and the pixel havingthe lightest density value from the following calculation.

In the region where the fingerprint is printed, an area occupied by thefingerprint ridge portion is different between the case when thefingerprint ridge is densely pressed and the case when the fingerprintridge is lightly pressed. Experientially in the majority of cases, thearea is included between approximately 20% and 80%.

So, a histogram is calculated from the region of the 10-pixel radius,the density value having a histogram cumulative value of 20% from thelightest density is assumed to be minD, and the density value having thehistogram cumulative value of 20% from the densest density is assumed tobe maxD, a threshold Th is defined as follows.Th=(minD+maxD)/2

Next, if the density of this remarked pixel is higher than the thresholdTh, it is determined as the black pixel, and if the density is lower, itis determined as the white pixel. The binary image can be generated byperforming this process on all pixels in the region where the ridgedirection is determined. The right side of FIG. 12 shows the ridgebinary image extracted in this way, for the fingerprint image on theleft side picture of FIG. 12. As understood from the right side of FIG.12, the black pixel in this ridge binary image includes not only thecandidate for the true ridge but also the candidate for the incipientridge line.

The extracted ridge binary image is stored in the data storing unit 22through the data processing and controlling unit 21.

(2) Step S32

Next, at the step S32 of FIG. 6, the ridge candidate pixel extractingunit 24 shown in FIG. 2 converts the ridge binary image into a skeletonand consequently extracts the pixel group which is supposed to be acandidate of the ridge. The reason why the skeleton image is used as theridge candidate pixel group is that in a process for distinguishing theridge and the incipient ridge, the treatment of the skeleton composed oflines having small widths is easier than that of the ridge having largerwidth.

The process for converting the binary image into the skeleton has beenproposed variously. In this embodiment, the simple strategy for usingthe ridge direction is employed. In short, at each black pixel, thetrace is carried out in the direction orthogonal to the ridge line, andit is judged whether or not the pixel is the central pixel in the blackpixels. If there is the possibility of the central pixel, it is left inits original state, and if it is not the central pixel, it is changed tothe white pixel. Although the thus-extracted skeleton pixel is not theskeleton in a strict sense to insure a linkage at a line width 1, it issufficient as the ridge candidate pixel. The right side of FIG. 13 showsthe image where the thus-extracted ridge candidate pixel groupoverlapped on the fingerprint image is displayed. The extracted ridgecandidate pixel group is stored in the data storing unit 22 through thedata processing and controlling unit 21.

(3) Step S33

Next, at the step S33 of FIG. 6, the incipient ridge feature extractingunit 25 shown in FIG. 2 extracts the incipient ridge feature for eachpixel in the ridge candidate pixel group. The incipient ridge featureimplies the characteristic feature to distinguish the incipient ridgeand the ridge, and the various kinds of incipient ridge features may beemployed. In this embodiment, the ridge width is employed as one of theincipient ridge features. The ridge width is known to be relatively widein the true ridge portion and relatively narrow in the incipient ridgeline portion. In extracting the ridge width, the ridge binary imageextracted at the step S31 is used. The ridge width can be easilyextracted by tracing the black pixel in both directions (twoorientations) crossing to the ridge direction from the ridge candidatepixel. Here, the direction crossing to the longitudinal direction (orlongitudinal direction) of the ridge line is used for processing.Preferably, the direction orthogonal to the longitudinal direction ofthe ridge line is used. The direction is referred to as the directionorthogonal to the ridge line, and both of the directions orthogonal tothe ridge are also referred to as the right and left directionsorthogonal to the ridge line.

The extracted incipient ridge feature is stored in the data storing unit22 through the data processing and controlling unit 21.

(4) Step S34

Next, at the step S34 of FIG. 6, the incipient ridge feature analyzingunit 26 shown in FIG. 2 analyzes the incipient ridge feature extractedfor the ridge candidate pixel group. The analysis of the incipient ridgefeature in this embodiment is carried out by tracing from the remarkedpixel to the right side in the direction orthogonal to the ridge andcomparing with the incipient ridge feature of the nearby ridge candidatepixel. This step is one of the main steps in this embodiment andexplained in detail with reference to FIGS. 13 to 15.

The left side picture of FIG. 13 is the enlarged view of the ridge imageincluding the thick incipient ridge lines. On the right side picture ofFIG. 13, the image same to the left side picture of FIG. 13 and theridge candidate pixel group are overlapped.

The left side picture of FIG. 13 will be explained below by using theremarked pixel area indicated as the rectangular frame in FIG. 13. FIG.14 shows the image where this remarked pixel area in the rectangularframe is rotated such that the ridge direction is vertical and thedirection orthogonal to the ridge is horizontal. On the right side ofFIG. 14, the ridge candidate pixel group is overlapped on the gray-scaleimage.

On the left side picture of FIG. 14, three ridge lines and two incipientridge lines are recognized. Typically, the incipient ridge line does notappear singularly. Usually the plurality of incipient ridge linesarranged in parallel appear in an area of a fingerprint. Thus, when theincipient ridge lines appear, the true ridges and the incipient ridgelines appear alternately in the direction orthogonal to the ridge. Alsoin this case, the incipient ridge features are alternately changed.Hence, if its pattern can be extracted, the incipient ridge line can berecognized.

In the case shown in the left side picture of FIG. 14, there are fiveridge lines arranged at substantially same intervals in the directionorthogonal to the longitudinal directions of the ridge lines. In somecases, all of the five ridge lines are the true ridge lines. In anothercase, 3 lines are the true ridge lines and the other two lines are theincipient ridge lines. In further another case, the two lines are thetrue ridge lines and the other three lines are the incipient ridgelines. In this embodiment, the incipient ridge features of the fiveridge lines are compared so that the true ridge lines and the incipientridge lines are distinguished. For example, when the five ridges arearranged, if there is the significant difference between the threeincipient ridge features of the first, third and fifth lines and the twoincipient ridge features of the second and fourth lines, the incipientridge lines are considered to be included. Here, the three incipientridge features of the first, third and fifth lines are referred to as anodd-numbered incipient ridge feature, and the two incipient ridgefeatures of the second and fourth lines are referred to as aneven-numbered incipient ridge feature.

FIG. 15 is a binary image of a part of the region in FIG. 14. In FIG.15, when P1 is assumed to be a remarked ridge candidate pixel, the ridgecandidate pixels from P2 to P5 are defined on the right side of FIG. 15.At this time, the three incipient ridge features of P1, P3 and P5 arethe odd-numbered incipient ridge features, and the two incipient ridgefeatures of P2 and P4 are the even-numbered incipient ridge features.

When the ridge width is employed as the incipient ridge feature, thejudgment of the true ridge and the incipient ridge, namely, theclassification of the ridge lines into true ridge lines and incipientridge lines can be carried out by applying the following condition.Here, the maximum value of the incipient ridge feature is defined asmaxFA. The maximum value and minimum value of the odd-numbered incipientridge features are defined as maxFO and minFO respectively. The maximumvalue and minimum value of the even-numbered incipient ridge featuresare defined as maxFE and minFE respectively. Also, 4 pitches (intervalsbetween the adjacent ridge lines) are measured between the five ridgelines, and the maximum value and minimum value of the four pitches aredefined as maxP and minP respectively.

Condition 1A: The case that the odd-numbered incipient ridge featurecorresponds with the true ridge line and the even-numbered incipientridge feature corresponds with the incipient ridge line,minFO>maxFE  1)(minFO−maxFE)/maxFA>thD1  2)minP/maxP>thP  3)Here, thD1 and thP are preset threshold parameters. From an actualfingerprint data, for example, thD1 is set to about 0.30 and thP is setto about 0.65 for the proper judgment.

The incipient ridge judgment process using this condition 1A isexplained bellow with reference to FIG. 15. In FIG. 15, when the ridgewidth is employed as the incipient ridge feature, the values (namely,number of pixels) of the incipient ridge features from P1 to P5 are 5,2, 5, 2 and 6, and as a result, maxFA=6, minFO=5 and maxFE=2.

Also, the four pitches between the five ridges are 4, 5, 4 and 6 (theirunits are pixels), and as a result, minP=4 and maxP=6. Thus,5>2  1)(5−2)/6>0.3  2)4/6>0.65  3)As mentioned above, all of the conditions are satisfied. As a result,the odd-numbered ridge lines are judged to be the true ridge lines, andthe even-numbered ridge lines are judged to be the incipient ridgelines.

In short, if the ridge width of the odd-numbered ridge is significantlylarger than the ridge width of the even-numbered ridge and their pitchesare substantially equal, the even-numbered ridge can be judged to be theincipient ridge line. As a result, the mark of the true ridge isassigned to P1, P3 and P5, and the mark of the incipient ridge line isassigned to P2 and P4.

In the case that the remarked pixel is in the incipient ridge line, theincipient ridge line can be judged by applying the following condition1B.

Condition 1B: The case that the odd-numbered ridge is the incipientridge line and the even-numbered ridge is the true ridge,maxFO<minFE  1)(minFE−maxFO)/maxFA>thD1  2)minP/maxP>thP  3)

This condition is the pattern opposite to the case of the condition 1Aso that its meaning is apparent from the above explanation of thecondition 1A.

This analysis is performed on all of the ridge candidate pixels. As theresult of the analysis, the true-ridge mark or incipient ridge mark orunclear mark is assigned to each of the all ridge candidate pixels.

This analysis result is stored in the data storing unit 22 through thedata processing and controlling unit 21.

(5) Step S35

Next, at the step S35 of FIG. 6, the data processing and controllingunit 21 of FIG. 2 monitors the progress of the extraction and analysis.When the extraction and analysis are finished for all the presetincipient ridge features, the process from the step S36 is executed.When the extraction and analysis are not finished, namely, there aresome incipient ridge features which are not analyzed yet, the processfrom the step S33 is executed again for the not-analyzed incipient ridgefeature.

The other example of the incipient ridge feature defined by using theridge direction density is explained below. In this case, theoperational flow from the step S33 of FIG. 6 is executed for extractingthe ridge direction density. The ridge direction density is calculatedas follows. The densities of the pixels located about ±5 pixels from theremarked pixel are traced to the ridge direction and their densities areaveraged. Typically, the ridge direction density of the incipient ridgeline portion is lower than the ridge direction density of the true ridgeportion.

Next, at the step S33 of FIG. 6, the incipient ridge feature analyzingunit 26 shown in FIG. 2 analyzes the incipient ridge feature extractedfrom the ridge candidate pixel group.

The process when the ridge direction density is employed as theincipient ridge feature is similar to the case that the ridge width isemployed as explained above. The conditional equations are similar.However the threshold values used to define the condition are preset tothe value suitable for the data property of the ridge direction density.

Condition 2A: The case that the odd-numbered ridge lines are the trueridge lines and the even-numbered ridge lines are the incipient ridgelines, the set of following equations represents the condition.minFO>maxFE  1)(minFO−maxFE)/maxFA>thD2  2)minP/maxP>thP  3)

Desirably all of the three equations should be satisfied.

Condition 2B: The case that the odd-numbered ridge lines are theincipient ridge lines and the even-numbered ridge lines are the trueridge lines,maxFO<minFE  1)(minFE−maxFO)/maxFA>thD2  2)minP/maxP>thP  3)

Here, thD2 and thP are the preset threshold parameters. From an actualfingerprint data, for example, thD2 is set to about 4.0 and thP is setto about 0.62 for the proper judgment.

(6) Step S36

When the processes for all of the incipient ridge features have beencompleted, at the step S36 of FIG. 6, the incipient ridge areaextracting unit 27 shown in FIG. 2 analyzes the ridge candidate pixelgroup to which the true-ridge mark or incipient-ridge-line mark isassigned and extracts the incipient ridge area.

The incipient ridge area extracting unit 27 traces about ±8 pixels fromthe remarked pixel in the ridge candidate pixel group in the ridgedirection among the remarked ridge candidate pixels and counts thenumber of marks assigned to the respective pixels and then calculatesthe ratio of the number of the true-ridge mark and the ratio of thenumber of the incipient-ridge-line mark against the number of all tracedpixels (17 pixels).

For a remarked pixel, if the traced assigned marks are only thetrue-ridge marks and when its ratio is equal to or higher than a certainthreshold, the remarked pixel is determined as the true ridge pixel. Thethreshold in this case is appropriately set to, for example, about 50%.If the traced assigned marks are only the incipient ridge marks and whenits ratio is equal to or higher than a certain threshold, the remarkedpixel is determined as the incipient ridge pixel. The threshold in thiscase is appropriately set to about 30%. This process is performed on allof the ridge candidate pixels.

Next, from each of the determined incipient ridge pixels (called 1stpixel), maximum of 10 pixels are traced on the right side in thedirection orthogonal to the ridge. When the ridge candidate pixel(called 2nd pixel) is found on the tracing and the 2nd pixel isdetermined to be in true ridge line, it is recognized that the 1st pixelis in the incipient ridge line and the 2nd pixel is in the true ridgeline. Further, it is recognized that there is no other true ridge andincipient ridge between the 1st pixel and the 2nd pixel. The midpoint ofthe 1st pixel and the 2nd pixel is calculated. Each of the pixelslocated between the 1st pixel and the midpoint are determined as truevalley pixel (in other word, “white pixel”). Each of the pixels locatedbetween the midpoint and the 2nd pixel are determined as true ridgepixel (“black pixel”). This determination process is also executed onthe left side in the direction orthogonal to the ridge.

This process is performed on all incipient ridge pixels. The pixel groupdetermined as the valley line pixels or true ridge pixels through thisprocess is determined as the incipient ridge area.

FIG. 16 shows an example of the thus-determined incipient ridge area.FIG. 16 is the result of the extraction of the incipient ridge area forthe fingerprint region shown in FIG. 7. In FIG. 16, black areas indicatethe pixel groups determined as the true ridge, and gray areas indicatethe pixel groups determined as the incipient ridge.

The thus-extracted incipient ridge areas are stored in the data storingunit 22 through the data processing and controlling unit 21.

Next, at the step S36 of FIG. 6, the incipient ridge area densitycorrecting unit 28 shown in FIG. 2 corrects the density value for thepixel group defined as the incipient ridge area and consequentlyextracts the image from which the incipient ridge line is removed.

At first, for the remarked pixel, an assumed ridge density and anassumed valley density in its nearby area are determined. The nearbyarea is desirably defined so that it includes both of the ridge and thevalley. For example, the area of about 16×16 pixels is defined as thenearby area in this case. The assumed ridge density may be defined asthe maximum density value. The assumed ridge density may be defines asthe density value that is lower by about 10% with respect to a histogramaccumulation value from the density maximum value for suppressing theinfluence of the noise. The assumed valley density may be defined as theminimum density value. The assumed valley density may be defined as thedensity value that is higher by about 10% with respect to the histogramaccumulation value from the minimum value for suppressing the influenceof the noise.

(7) Step S37

Next, the mark attached to the remarked pixel is checked. If theremarked pixel is the valley pixel (white pixel), the density of thepixel is replaced with an assumed valley density value. By thisreplacement, the valley is clearly enhanced. If the remarked pixel isthe true ridge pixel (black pixel), the density of the pixel is replacedwith an assumed ridge density value. By this replacement, the true ridgeis clearly enhanced. Namely, a corrected image is generated byapproximating densities of pixels classified as said incipient ridges inStep S34 close to the density of valley lines in the fingerprint image.This process is performed on all of the pixels within the incipientridge area. FIG. 17 shows the thus-corrected ridge image. From FIG. 17,it is known that the density value within the incipient ridge area shownin FIG. 16 is corrected and that the pixel group judged as the incipientridge line is removed.

The image which is corrected as mentioned above and from which theincipient ridge line is removed is stored in the data storing unit 22through the data processing and controlling unit 21 and then sent to theminutia extracting unit 16. Here, we come to the end of the detailedexplanation of the embodiment of the incipient ridge extracting andeliminating unit 13.

The operations of the pore extracting and eliminating unit 14 of FIG. 1will be described below in detail with reference to the configurationview of FIG. 3, a flowchart of FIG. 18 and related views of FIGS. 19 to27.

FIG. 19 is an example of the fingerprint image having the thick sweatpores, and this image is used in the following explanation of the sweatpore extracting and eliminating process. If the sweat pores existcontinuously or become enlarged, the line including the sweat pore iserroneously recognized as a valley line. As a result, two ridge linesare erroneously extracted from only one true ridge line. This results inthe occurrence of many false minutiae. FIG. 20 shows an example of suchfalse minutiae.

The left side picture of FIG. 21 is the enlarged view of the ridge imageincluding the thick sweat pores. Then, on the right view of FIG. 21, theskeleton image extracted from the ridge image on the left side pictureof FIG. 21 is overlapped.

Also, on the left side picture of FIG. 22, after the region where thesweat pores are thick on the left side picture of FIG. 21 is cut away,the region is rotated such that the ridge direction becomes vertical andthe direction orthogonal to the ridge becomes horizontal.

At the time when the sweat pore extracting and eliminating process isstarted, the already-inputted fingerprint image and thealready-extracted ridge direction data are stored in the data storingunit 32 of FIG. 3.

(1) Step S41

At the step S41 of FIG. 18, the valley binary image extracting unit 33shown in FIG. 3 extracts the binary image in which the valley portion isrepresented by “1” and the ridge portion by “0”. In the valley linebinary image, the “1” of the valley portion is referred to as the blackpixel, and the “0” of the ridge portion is referred to as the whitepixel.

The extraction of the valley line binary image may be carried out byextracting the ridge binary image after performing the density inversionon the usual image in which the ridge line is printed in black. Thedensity inversion is carried out by defining a complement of 255corresponding to the density value of each pixel as the density value ofthe inversion image. FIG. 23 shows the image where the density inversionis performed on the image on the left side picture of FIG. 22.

The ridge binary image extracting process for the image after thedensity inversion can be performed in the same manner as the ridgebinary image extracting unit 23 in the incipient ridge extracting andeliminating unit 13 explained before.

The thus-extracted valley line binary image is stored in the datastoring unit 32 through the data controlling unit 31.

(2) Step S42

Next, at the step S42 of FIG. 18, the ridge candidate pixel extractingunit 34 shown in FIG. 3 converts the valley line binary image into askeleton and consequently extracts the pixel group supposed to be acandidate of the valley line. The reason why the skeleton image is usedas the valley candidate pixel group is that in a process fordistinguishing the valley line and the line having the sweat pore, thetreatment of the skeleton having a smaller width is easier than that ofthe valley line having larger width.

The process for converting the binary image into the skeleton can becarried out by using the already explained method performed by the ridgecandidate pixel extracting unit 24 in the incipient ridge extracting andeliminating unit 13. The right side picture of FIG. 22 shows the imagewhere the thus-extracted valley candidate pixel group (skeleton image)overlapped on the fingerprint image is displayed.

(3) Step S43

Next, at the step S43 of FIG. 18, the pore feature extracting unit 35shown in FIG. 3 extracts the sweat pore feature, for each pixel in thevalley candidate pixel group. The sweat pore feature implies thecharacteristic feature to distinguish the sweat pore and the valleyline, and the various kinds of sweat pore features may be employed. Inthis embodiment, the ridge direction density is employed as one of thesweat pore features. The ridge direction density is obtained by tracingabout ±5 pixels from the remarked pixel to the ridge direction and thenaveraging their densities. Typically, the ridge direction density of thesweat pore portion is higher than the ridge direction density of thetrue valley line portion.

(4) Step S44

Next, at the step S44 of FIG. 18, the pore feature analyzing unit 36shown in FIG. 3 analyzes the sweat pore feature extracted from thevalley candidate pixel group. The analysis of the sweat pore feature inthis embodiment is carried out by tracing from the remarked pixel to theright side in the direction orthogonal to the ridge and then comparingwith the sweat pore feature of the nearby valley candidate pixel. Thisstep is one of the main steps of this embodiment. Thus, this isexplained in detail with reference to FIGS. 24 to 25.

The left side picture of FIG. 24 is the view where the remarked areashown on the left side picture of FIG. 22 is enlarged and includes ridgelines and sweat pores. On the right side picture of FIG. 24, the imagesame to the left side picture of FIG. 24 and the valley candidate pixelgroup are overlapped.

In the left side picture of FIG. 24, three valley lines and two sweatpores are recognized. Typically, the sweat pore which cause the falseminutia does not appear singularly. Usually a plurality of sweat poresappear in an area of a fingerprint. Thus, when the sweat pores appear,the true valley lines and the sweat pores appear alternately in thedirection orthogonal to the ridge. In this case, the sweat pore featuresare alternately changed. Hence, if its pattern can be extracted, thesweat pore can be recognized.

In the case shown in the left side picture of FIG. 24, there are fivevalley lines arranged at substantially same interval in the directionorthogonal to the longitudinal directions of the valley line. In somecases, all five valley lines are the true valleys. In another case, 3valleys are the true valleys and the other two valleys are the sweatpores. In further another case, the two valleys are the true valleys andthe three valleys are the sweat pores. In this embodiment, the sweatpore features of the valleys are compared so that the true valley linesand the sweat pores are distinguished. For example, when the fivevalleys are arranged, if there is the significant difference between thethree sweat pore features of the first, third and fifth lines and thetwo sweat pore features of the second and fourth lines, the sweat poresare considered to be included. Here, the three sweat pore features ofthe first, third and fifth lines are referred to as an odd-numberedsweat pore feature, and the two sweat pore features of the second andfourth lines are referred to as an even-numbered sweat pore feature.

The upper picture of FIG. 25 is the view where a part of the region onthe right side picture of FIG. 24 including valley lines is enlarged. Inthe upper view of FIG. 25, when P1 is assumed to be a remarked pixel inthe valley candidate pixel group, the valley candidate pixels from P2 toP5 are defined on the right side of the remarked pixel. At this time,the three sweat pore features of P1, P3 and P5 are the odd-numberedsweat pore features, and the two sweat pore features of P2 and P4 arethe even-numbered sweat pore features.

When the ridge direction density is employed as the sweat pore feature,the judgment of the true valley line and the sweat pore, namely, theclassification of the valley line into true valley lines and sweat porescan be carried out by applying the following condition. Here, themaximum value of the sweat pore feature is defined as maxFA. The maximumvalue and minimum value of the odd-numbered sweat pore features aredefined as maxFO and minFO respectively. The maximum value and minimumvalue of the even-numbered sweat pore features are defined as maxFE andminFE respectively. Also, 4 pitches (intervals between the adjacentvalley lines) are measured between the five valley lines, and themaximum value and minimum value of the four pitches are defined as maxPand minP respectively.

Condition 3A: The case that the odd-numbered valley line is the truevalley line and the even-numbered valley line is the sweat pore line,maxFO<minFE  1)(minFE−maxFO)/maxFA>thD3  2)minP/maxP>thP  3)Here, thD3 and thP are preset threshold parameters. From an actualfingerprint data, for example, thD3 is set to about 0.2 and thP is setto about 0.65 for the proper judgment.

The sweat pore judgment process using this condition 3A is explainedbellow with reference to FIG. 25. In the upper image of FIG. 25, thedensity of each valley line indicates the ridge direction density. Also,the downside image of FIG. 25 indicates the densities of valley lines asa histogram. As recognized from the downside image of FIG. 25, theminimum value of the even-numbered sweat pore feature is higher than themaximum value of the odd-numbered sweat pore feature by 30% or more, andthe pitches between the adjacent valley lines are substantially equal.Thus, all of the condition 3A is satisfied. As a result, theodd-numbered valley lines are judged to be the true valley lines, andthe even-numbered valley lines are judged to be the sweat pore lines.

Thus, the mark of the true valley line is assigned to P1, P3 and P5, andthe mark of the sweat pore line is assigned to P2 and P4.

In the case that the remarked pixel is in the sweat pore line, the sweatpore line can be judged by applying the following condition 3B.

Condition 3B: The case that the odd-numbered valley line is the sweatpore line and the even-numbered valley line is the true valley line,minFO>maxFE  1)(minFO−maxFE)/maxFA>thD3  2)minP/maxP>thP  3)

This condition is the pattern opposite to the case of the condition 3Aso that its meaning is apparent from the above explanation about thecondition 3A.

The above mentioned analysis processing is performed on all of thevalley candidate pixels. As the result of the analysis, the true-valleymark or sweat-pore mark or unclear mark is assigned to all of the valleycandidate pixels.

This analysis result is stored in the data storing unit 32 through thedata controlling unit 31.

(5) Step S45

Next, at the step S45 of FIG. 18, the data controlling unit 31 of FIG. 3monitors the progress of the extraction and analysis. When theextraction and analysis are finished for all the preset sweat porefeatures, the process from the step S46 is executed. When the extractionand analysis are not finished, the process from the step S45 is executedagain for the not-analyzed sweat pore feature.

The other example of the sweat pore feature defined by using a densitydifference accumulated value is explained below. In this case, theoperational flow from the step S43 of FIG. 18 is executed for extractingthe density difference accumulated value. The density differenceaccumulated value is calculated as follows. The densities of the pixelslocated in the valley candidate pixel group and about ±8 pixels from theremarked pixel are traced to the ridge direction and the densitydifference between the every adjacent traced pixels are averaged.Typically, at the sweat pore portion, the sweat pore having the smalldensity value and the ridge having the great density value appearalternately, thereby the density difference accumulated value becomeshigh. On the other hand, since the true valley line has theapproximately uniform density values, the density difference accumulatedvalue becomes small.

Next, at the step S44 of FIG. 18, the pore feature analyzing unit 36shown in FIG. 3 analyzes the sweat pore feature of the densitydifference accumulated value extracted from the valley candidate pixelgroup.

The process when the density difference accumulated value is employed asthe sweat pore feature is similar to the case that the ridge directiondensity is employed as explained above. The conditional equations aresimilar. However the threshold values used to define the condition arepreset to the value suitable for the data property of the densitydifference accumulated value.

Condition 4A: The case that the odd-numbered valley lines are the truevalley lines and the even-numbered valley lines are the sweat porelines,maxFO<minFE  1)(minFE−maxFO)/maxFA>thD4  2)minP/maxP>thP  3)

Condition 4B: The case that the odd-numbered ridge lines are the sweatpore lines and the even-numbered ridge lines are the true valley lines,minFO>maxFE  1)(minFO−maxFE)/maxFA>thD4  2)minP/maxP>thP  3)

Here, thD4 and thP are the preset threshold parameters. From an actualfinger print data, for example, thD4 is set to about 0.25 and thP is setto about 0.65 for the proper judgment.

(6) Step S46

When the process for all of the sweat pore features has been completed,at the step S46 of FIG. 18, the pore area extracting unit 37 shown inFIG. 3 analyzes the valley candidate pixel group to which thetrue-valley mark or sweat-pore mark is assigned, and then extracts thesweat pore area.

The pore area extracting unit 37 traces about ±8 pixels in the valleycandidate pixel group from the remarked valley candidate pixel to theridge direction, counts the number of marks assigned to the respectivepixels and then calculates the ratio of the number of the true-valleymark and the ratio of the sweat-pore mark against the number of alltraced pixels (17 pixels).

For a remarked pixel, if the traced assigned marks are only thetrue-valley marks and its ratio is equal to or higher than a certainthreshold, the remarked pixel is determined as the true valley pixel.The threshold in this case is appropriately set to, for example, about50%. If the traced assigned marks are only the sweat-pore mark and itsratio is equal to or higher than a certain threshold, the remarked pixelis determined as the sweat pore pixel. The threshold in this case isappropriately set to about 20%.

This process is performed on all of the valley candidate pixels.

Next, for each of the determined sweat pore pixels (called 1st pixel),maximum of 10 pixels are traced on the right side in the directionorthogonal to the ridge. When the valley candidate pixel (called 2ndpixel) is found on the tracing and the 2nd pixel is determined to be intrue valley line, it is recognized that the 1st pixel is in the sweatpore line. Further, it is recognized that there is no other true valleyline and sweat pore line between the 1st pixel and the 2nd pixel.

The midpoint of the 1st pixel and the 2nd pixel is calculated. Each ofthe pixels located between the 1st pixel and the midpoint are determinedas true ridge line (in other word, “black pixel”). Each of the pixelslocated between the midpoint and the 2nd pixel are determined as truevalley pixel (“white pixel”). This determination process is alsoexecuted on the left side in the direction orthogonal to the ridge.

This process is performed on all of the incipient ridge pixels. Thepixel group determined as the true ridge pixels or the true valleypixels through this process is determined as the sweat pore area.

FIG. 26 shows an example of the thus-determined sweat pore area. FIG. 26is the result of the extraction of the sweat pore area for thefingerprint region shown in FIG. 19. In FIG. 26, black areas indicatesthe pixel group determined as the true ridge although the sweat poresare included, and gray areas indicates the pixel group determined as thetrue valley line. The thus-extracted sweat pore area is stored in thedata storing unit 32 through the data controlling unit 31.

(7) Step S47

Next, at the step S47 of FIG. 18, the pore area density correcting unit38 shown in FIG. 3 corrects the density value for the pixel groupdefined as the sweat pore area and consequently extracts the image fromwhich the sweat pore is removed.

At first, for the remarked pixel, an assumed ridge density and anassumed valley density in its nearby area are determined. This processis substantially same to the explanation about the incipient ridge areadensity correcting unit 28 in the incipient ridge extracting andeliminating unit 13.

Next, the mark attached to the remarked pixel is checked. If theremarked pixel is the black pixel, the density of the pixel is replacedwith an assumed ridge density value. By this replacement, the ridge isclearly enhanced. If the remarked pixel is the white pixel, the densityof the pixel is replaced with an assumed valley density value. By thisreplacement, the true valley is clearly enhanced. Namely, a correctedimage data is generated by approximating densities of pixels classifiedinto said pores close to the density of true ridges in the fingerprintimage. This process is performed on all of the pixels within the sweatpore area. FIG. 27 shows the thus-corrected sweat pore image. From FIG.17, it is known that the density value in the sweat pore area in FIG. 26is corrected and that the pixel group judged as the sweat pore isremoved.

The image which is corrected as mentioned above and from which the sweatpore is removed is stored in the data storing unit 32 through the datacontrolling unit 31 and then sent to the minutia extracting unit 16.Here, we come to the end of the detailed explanation of the embodimentof the pore extracting and eliminating unit 13.

The operations of the ridge extracting and accentuating unit 15 shown inFIG. 1 will be described below in detail with reference to theconfiguration view and the flowchart of FIG. 28. The ridge extractingand accentuating unit 15 has many functions which are common with theincipient ridge extracting and eliminating unit 13 and the poreextracting and eliminating unit 14.

In the ridge extracting and accentuating process, the insight that inthe analysis of the incipient ridge feature executed in the process forextracting the incipient ridge, when there is no significant differencesbetween the incipient ridge features of ridge lines arranged in theorthogonal direction of ridge lines, all the ridge lines are able to bejudged to be true ridge lines, is used.

Also the insight that in the analysis of the pore feature executed inthe process for extracting the pores, when there is no significantdifferences between the pore features of valley lines arranged in theorthogonal direction of ridge lines, all the valley lines are able to bejudged to be true valley lines, is used.

At the time when the ridge extracting and accentuating process isstarted, the already-inputted fingerprint image and thealready-extracted ridge direction data are stored in the data storingunit 42 of FIG. 4.

At the step S51 of FIG. 28, the ridge binary image extracting unit 43shown in FIG. 4 extracts the binary image in which the ridge portion isrepresented by “1” and the valley portion by “0”. This digitalizationprocess can be executed by applying the already explained methodemployed by the ridge binary image extracting unit 23 in the incipientridge extracting and eliminating unit 13.

Next, at the step S52 of FIG. 28, the ridge candidate pixel extractingunit 44 shown in FIG. 4 converts the ridge binary image into theskeleton and consequently extracts the pixel group that which issupposed to be a candidate of the ridge. This process can be executed byapplying the already explained method employed by the ridge candidatepixel extracting unit 24 in the incipient ridge extracting andeliminating unit 13.

Next, at the step S53 of FIG. 28, the incipient ridge feature extractingunit 45 shown in FIG. 4 extracts the incipient ridge feature, for eachpixel in the incipient ridge candidate pixel group. This process can beexecuted by applying the already explained method employed by theincipient ridge feature extracting unit 25 in the incipient ridgeextracting and eliminating unit 13.

Next, at the step S54 of FIG. 28, the incipient ridge feature analyzingunit 46 shown in FIG. 4 analyzes the incipient ridge feature extractedfrom the ridge candidate pixel group. This process can be executed byapplying the already explained method employed by the incipient ridgefeature analyzing unit 26 in the incipient ridge extracting andeliminating unit 13. However, the condition is set as follows.

When the ridge width is employed as the incipient ridge feature and allof the following conditional equations are satisfied, all of the ridgescan be judged to be the true ridges. Here, the maximum value and minimumvalue of the incipient ridge features are defined as maxFA and minFArespectively. Also, the maximum value and minimum value of the ridgepitches are defined as maxP and minP respectively.

Condition 5: The condition for judging all of the ridges to be the trueridges,minFA/maxFA>thD5  1)minP/maxP>thP  2)maxP<thX  3)

Here, thD5, thP and thX are the preset threshold parameters. From anactual fingerprint data, for example, thD5 is set to about 0.9, thP isset to about 0.65 and thX is set to about 20 for the proper judgment.The maximum pitch is set within the range appearing in the usualfingerprint (less than 20 pixels).

This condition implies that all of the ridges can be judged to be thetrue ridges, if (1) the ridge widths of the nearby ridges aresubstantially equal, (2) the differences of the pitches between adjacentridges are small and (3) the maximum pitch is in the normal range. Themark of the true ridge is assigned to all of the ridge candidate pixelssatisfying this condition.

This analysis is performed on all of the ridge candidate pixels. As theresult of the analysis, the true-ridge mark or unclear mark is assignedto each of the all ridge pixels. This analysis result is stored in thedata storing unit 42 through the data controlling unit 41.

(5) Step S55

Next, at the step S55 of FIG. 28, the data controlling unit 41 of FIG. 4monitors the progress of the extraction and analysis. When theextraction and analysis are finished for all of the preset incipientridge features, the process from the step S56 is executed. When theextraction and analysis are not finished, namely, there are someincipient ridge features which are not analyzed yet, the process fromthe step S53 is executed again for the not-analyzed incipient ridgefeature.

The other example of the incipient ridge feature defined by using theridge direction density is explained below. In this case, the ridgedirection density is extracted by the process explained from the stepS53 of FIG. 28.

Next, at the step S54 of FIG. 28, the incipient ridge feature analyzingunit 56 shown in FIG. 4 analyzes the incipient ridge feature extractedfrom the ridge candidate pixel group.

The process when the ridge direction density is employed as theincipient ridge feature is similar to the case when the foregoing ridgewidth is employed as the incipient ridge feature. The conditionalequations are also substantially equal. However, the threshold valuesare changed for its data property.

Condition 6: The condition for judging all of the ridges to be the trueridges,minFA/maxFA>thD6  1)minP/maxP>thP  2)maxP<thX  3)

Here, thD6, thP and thX are the preset threshold parameters. From anactual fingerprint data, for example, thD6 is set to about 0.9, thP isset to about 0.65 and thX is set to about 20 for the proper judgment.

(6) Step S56

When the process for all of the incipient ridge features has beencompleted, the operational flow proceeds to the step S56 of FIG. 28. Atthe step S56, the ridge area extracting unit 47 shown in FIG. 4 analyzesthe ridge candidate pixel group to which the true-ridge mark is assignedfor extracting the ridge area. The ridge area extracting unit 47 tracesabout ±8 pixels in the ridge candidate pixel group from the remarkedridge candidate pixel to the ridge direction and counts the number ofmarks assigned to the respective pixels and then calculates the ratio ofthe number of the true-ridge mark against the number of all tracedpixels (17 pixels).

If the ratio of the true-ridge mark is equal to or higher than a certainthreshold, the remarked pixel is determined to be a true ridge pixel. Itis proper that the threshold in this case is about 50%. This process isperformed on all of the ridge candidate pixels.

Next, from each of the pixels determined as the true ridge (called 1stpixel), maximum of thX (which is a preset threshold parameter) pixelsare traced on the right side in the direction orthogonal to the ridge.When the ridge candidate pixel (called 2nd pixel) is found on thetracing and the 2nd pixel is determined to be in true ridge line, the1st pixel and the 2nd pixel are recognized to be in true ridges and avalley exists between the 1st pixel and the 2nd pixel.

We define here the 3rd, 4th and 5th pixels. Assume that the lineconnecting the 1st pixel and the 2nd pixel is divided into four pieceshaving equal length. By the division, 3 intermediate points are set.They are called 3rd, 4th and 5th pixels in order from the intermediatepoint nearest to the 1st point, respectively. Then, each of the pixelslocated between the 1st pixel and the 3rd pixel are determined as trueridge pixels. Each of the pixels located between the 5th pixel and the2nd pixel are determined as true ridge pixels. Each of the pixelslocated between the 3rd pixel and the 5th pixel are determined as valleypixels (namely, white pixels). Next, the same process is also performedon the left side in the direction orthogonal to the ridge.

This process is performed on all of the ridge pixels. As a result, thepixel group determined as the ridge pixels or valley line pixels servesas the ridge area. The thus-extracted ridge area is stored in the datastoring unit 42 through the data controlling unit 41.

(7) Step S57

Next, at the step S57 of FIG. 28, the ridge area density correcting unit48 shown in FIG. 4 corrects the density value for the pixel groupdefined as the ridge area and consequently extracts the image where theridge lines and the valley lines are enhanced. The emphasizing processemployed by the ridge area density emphasizing unit 48 can be executedas already explained.

(1) Step S61

Next, at the step S61 of FIG. 28, the valley binary image extractingunit 49 shown in FIG. 4 extracts the binary image in which a valley lineportion is represented by “1” and a ridge portion by “0”. Thisdigitalization process can be executed by applying the already explainedmethod employed by the valley binary image extracting unit 33 in thepore extracting and eliminating unit 14.

(2) Step S62

Next, at the step S62 of FIG. 28, the valley candidate pixel extractingunit 50 shown in FIG. 4 converts the valley line binary image into askeleton and consequently extracts the pixel group that is supposed tobe a candidate of the valley line. The extraction can be executed byapplying the already explained method employed by the valley candidatepixel extracting unit 34 in the pore extracting and eliminating unit 14.

(3) Step S63

Next, at the step S63 of FIG. 28, the pore feature extracting unit 51shown in FIG. 4 extracts the sweat pore feature for each pixel in thevalley candidate pixel group. This extraction process can be executed byapplying the already explained method employed by the pore featureextracting unit 35 in the pore extracting and eliminating unit 14.

(4) Step S64

Next, at the step S64 of FIG. 28, the pore feature analyzing unit 52shown in FIG. 4 analyzes the sweat pore feature extracted from thevalley candidate pixel group. This extraction process can be executed byapplying the already explained method employed by the pore featureanalyzing unit 36 in the pore extracting and eliminating unit 14.However, the condition is set as follows.

When the ridge direction density is employed as the sweat pore featureand all of the following conditional equations are satisfied, all of thevalley lines can be judged to be the true valley lines. Here, themaximum value and minimum value of the sweat pore features are definedas maxFA and minFA respectively. Also, the maximum value and minimumvalue of the valley line pitches are defined as maxP and minP.

Condition 7: The condition for judging that all of the valley lines arethe true valley lines,minFA/maxFA>thD7  1)minP/maxP>thP  2)maxP<thX  3)

Here, thD7, thP and thX are the preset threshold parameters. From anactual fingerprint data including five valley candidate lines, forexample, thD7 is set to about 0.8, thP is set to about 0.65 and thX isset to about 20 for the proper judgment.

This condition implies that all of the valley lines can be judged to bethe true valley lines, if (1) the ridge direction densities of theadjacent valley lines are substantially equal, (2) the differences ofthe pitches between adjacent valleys are small and (3) the maximumpitches between adjacent valleys is within 20 pixels. The mark of thetrue valley line is assigned to all of the valley candidate pixelssatisfying this condition.

This analysis is performed on all of the valley candidate pixels. As theresult of the analysis, the true-valley mark or unclear mark is assignedto each of the all valley line pixels. This analysis result is stored inthe data storing unit 42 through the data controlling unit 41.

(5) Step S65

Next, at the step S65 of FIG. 28, the data controlling unit 41 of FIG. 4monitors the progress of the extraction and analysis. When theextraction and analysis are finished for all of the preset sweat porefeatures, the process from the step S66 is executed. When the extractionand analysis are not finished, namely, there are some sweat porefeatures which are not analyzed yet, the process from the step S63 isexecuted again for the not-analyzed sweat pore feature.

The other example of the sweat pore feature defined by using the densitydifference accumulated value is explained below. In this case, thedensity difference accumulated value is extracted by the process fromthe step S63 of FIG. 28. The extraction process can be executed byapplying the already explained method employed by the pore extractingand eliminating unit 14.

Next, at the step S64 of FIG. 28, the pore feature analyzing unit 52shown in FIG. 4 analyzes the sweat pore feature extracted for the valleycandidate pixel group.

Also, the process when the density difference accumulated value isemployed as the sweat pore feature is substantially equal to the casewhen the foregoing ridge direction density is employed. The conditionalequations are also substantially equal. However, the threshold valuesare changed for its data property.

Condition 8: The condition for judging all of the valley lines to be thetrue valley lines,minFA/maxFA>thD8  1)minP/maxP>thP  2)maxP<thX  3)

Here, thD8, thP and thX are the preset threshold parameters. From anactual fingerprint data including five valley candidate lines, forexample, thD8 is set to be about 0.95, thP is set to be about 0.65 andthX is set to be 20 for the proper judgment.

(6) Step S66

When the process for all of the sweat pore features has been completed,the operation flow proceeds to the step S66 of FIG. 28. At the step S66,the valley area extracting unit 53 shown in FIG. 4 analyzes the valleycandidate pixel group to which the true-valley mark is assigned, andthen extracts the valley area.

The pixels in the valley candidate pixel group and ±8 pixels from theremarked valley candidate pixel in the direction of ridge line aretraced and the number of marks assigned to the respective pixels arecounted and the ratio of the number of the true-valley mark against thenumber of all traced pixels (17 pixels) is calculated.

If the ratio of the true-valley mark is equal to or higher than acertain threshold, the remarked pixel is determined to be a true valleypixel. It is proper that the threshold in this case is about 50%. Thisprocess is performed on all of the valley candidate pixels.

Next, from each of the pixels determined as the true valley (called 1stpixel), thX (which is a preset threshold parameter) pixels are traced onthe right side in the direction orthogonal to the ridge. When the valleycandidate pixel (called 2nd pixel) is found on the tracing and the 2ndpixel is determined to be in true valley line, the 1st pixel and the 2ndpixel are recognized to be in true valley lines and a ridge existsbetween the 1st pixel and the 2nd pixel.

We define here the 3rd, 4th and 5th pixels. Assume that the lineconnecting the 1st pixel and the 2nd pixel is divided into four pieceshaving equal length. By the division, 3 intermediate points are set.They are called 3rd, 4th and 5th pixels in order from the intermediatepoint nearest to the 1st point, respectively. Then, each of the pixelslocated between the 1st pixel and the 3rd pixel are determined as truevalley pixels (namely, white pixels). Each of the pixels located betweenthe 5th pixel and the 2nd pixel are determined as true valley pixels(white pixels). Each of the pixels located between the 3rd pixel and the5th pixel are determined as ridge pixels (black pixels). Next, the sameprocess is also performed on the left side in the direction orthogonalto the ridge.

This process is performed on all of the true valley pixels. As a result,the pixel group determined as the ridge pixels or valley line pixelsserves as the valley area. The thus-extracted valley area is stored inthe data storing unit 42 through the data controlling unit 41.

(7) Step S67

Next, at the step S67 of FIG. 28, the valley area density accentuatingunit 54 shown in FIG. 4 corrects the density value for the pixel groupdefined as the valley line area and consequently extracts the imagewhere the ridge and the valley lines are enhanced. The emphasizingprocess of the valley area density accentuating unit 54 can be executedby that of the ridge area density emphasizing unit 48. Since theoperation of the valley area density accentuating unit 54 and theoperation of the ridge area density emphasizing unit 48 are similar, theembodiment in which those two units are unified into a common unit(realized by a common hardware unit or a common software program) iseasily constructed from the above explanation for those skilled in theart.

The reason why those two units are separated in this embodiment is thatthe former process of ridge extraction based on the incipient ridgefeature analysis and the latter process of valley line extraction basedon the sweat pore feature analysis can be configured independently ofeach other without any dependency relation. The image where the ridgelines and the valley lines are enhanced in this way is stored in thedata storing unit 42 through the data controlling unit 41 and then sentto the minutia extracting unit 16. Here, the detailed explanation of theembodiment of the ridge extracting and accentuating unit is completed.

The process explained in the present embodiment can be applied not onlyto the fingerprint image but also to palm-print images. Generally theprocess can be applied to any kind of images having stripes the shapesof which are similar to the incipient ridge or the sweat pore.

Also, the present embodiment has been explained by using the ridgeenhancing mask example and the various parameter examples under theassumption of the normally employed fingerprint image of 500 dpi.However, the above explained process can be adapted even for thefingerprint image scanned at a resolution other than 500 dpi by usingthe mask or parameter suitable for the resolution.

Also, even in the case of employing the other definition of featurequantity as the incipient ridge feature or the sweat pore feature, theincipient ridge line and the sweat pore can be recognized by extractingthe pattern of the feature which is alternately varied in the directionorthogonal to the ridge.

Moreover, in another embodiment, a white pixel radius can be employed asthe different sweat pore feature. This embodiment will be describedbelow in detail with reference to FIGS. 29 to 31.

FIG. 29 is an example of the fingerprint image. In this image, sweatpores are enlarged and the plurality of sweat pores are connected toform continuous sweat pore portions. This kind of continuous sweat poreportions tends to induce the erroneous extraction of false minutiae. Theleft side picture of FIG. 30 is the enlarged view of the ridge imageincluding the enlarged pore, in the image where the focused area shownin FIG. 29 is cut away and rotated such that the direction orthogonal tothe ridge becomes horizontal. On the right side of FIG. 30, the pictureof the left side of FIG. 30 and the valley candidate pixel group areoverlapped.

The white pixel radius is the feature for extracting the enlarged poreand indicates the size of a circle contained in the enlarged pore by itsradius. For carrying out the calculation, the pixel group having themaximum density in the direction orthogonal to the ridge is extracted.At first, as an initial value, all of the pixels are set to be whitepixels. Next, if the density value of a certain remarked pixel is themaximum or the second density value, in the pixel group including about8 pixels from the remarked pixel to the right and left sides in thedirection orthogonal to the ridge, the remarked pixel is defined to beblack.

When such a process is performed on all of the pixels, the binary imagewhere the pixels having the relatively high density in the directionorthogonal to the ridge are black pixels is generated. This binary imageis referred to as the maximum density pixel group. FIG. 31 is themaximum density pixel group generated from the right side picture ofFIG. 30.

Next, each pixel in the valley candidate pixel group is put on the imageshown in FIG. 31, and a white pixel radius feature is extracted for eachpixel of the valley candidate pixel group.

When the remarked pixel is defined as P drawn in FIG. 31, the maximumcircle including only white pixels determined from the circle with the Pas a center, and its radius is extracted as the white pixel radius.

In the fingerprint image where the enlarged pores are thick, the whitepixel radius has the large value in the enlarged pore portion, and thevalue of the white pixel radius is relatively small in the true valleyline portion.

In the case that the white pixel radius is employed as the sweat porefeature, whether the valley is a true valley or a sweat pore can bejudged in accordance with the following condition. Here, the maximumvalue of the sweat pore features is defined as maxFA. The maximum valueand minimum value of the odd-numbered sweat pore features are defined asmaxFO and mini FO respectively. The maximum value and minimum value ofthe even-numbered sweat pore features are defined as maxFE and minFErespectively. Also, there are 4 pitches between the any adjacent twovalley lines selected from total of 5 lines, and the maximum value andminimum value of the four pitches are defined as maxP and minPrespectively.

Condition 9: The condition for judging that the odd-numbered valley lineis true valley lines and the even-numbered valley line is sweat porelines,maxFO<minFE  1)(minFE−maxFO)/maxFA>thD9  2)minP/maxP>thP  3)

Here, thD9 and thP are the preset threshold parameters. From an actualfingerprint data, for example, thD9 is set to about 0.4 and thP is setto about 0.65 for the proper judgment.

The right side of FIG. 32 shows the sweat pore area that is extractedfrom the image on the left side picture of FIG. 32 by using the whitepixel radius as the sweat pore feature.

In the above mentioned embodiments, the conditional judgment is carriedout only for a single incipient ridge feature or the sweat pore feature.However, another embodiment may be considered in which the conditionsfor the plurality of properties are judged by combining the logical sumor the logical product. In this case, the more accurate incipient ridgeline extraction and sweat pore extraction can be attained with theappropriately set threshold parameters.

In the already explained embodiments, when analyzing the incipient ridgefeature and the sweat pore feature, the five ridge candidates and valleyline candidates are used as examples. However, those numbers are notlimited to 5. As those numbers are increased, the more accurateincipient ridge line judgment and sweat pore judgment can be attained.In the viewpoint that the applicable region can be extracted from manyplaces of the fingerprints, 5 to 7 candidate lines are suitable.

In the already explained embodiments, the system for recognizingfingerprint image may be attained by using computer sets connected toeach other and each of which corresponds to the incipient ridgeextracting and eliminating unit 13, the pore extracting and eliminatingunit 14 and the ridge extracting and accentuating unit 15. Or, thesystem may be designed such that one computer set includes apparatuses,each of which corresponds to the fingerprint image inputting unit 11,the ridge direction extracting unit 12, the incipient ridge extractingand eliminating unit 13, the pore extracting and eliminating unit 14,the ridge extracting and accentuating unit 15, the minutia extractingunit 16 and the minutia outputting unit 17. Or, the system maybedesigned so as to instruct a computer by a computer program to executethe functions of the fingerprint image inputting unit 11, the ridgedirection extracting unit 12, the incipient ridge extracting andeliminating unit 13, the pore extracting and eliminating unit 14, theridge extracting and accentuating unit 15, the minutia extracting unit16 and the minutia outputting unit 17.

The explained embodiments have the following features.

(1) The configuration for extracting the minutiae from a fingerprintimage has a means for extracting the incipient ridge feature from theridge and the incipient ridge; a means for analyzing the incipient ridgefeature of some several ridges in the direction orthogonal to the ridgeand judging the ridge to be the incipient ridge line when the incipientridge feature pattern agrees with a preset condition; a means forextracting the incipient ridge area by using the ratio of the number ofthe pixels judged to be an incipient ridge line; and a means forcorrecting the pixel density inside the incipient ridge area.

(2) The configuration for extracting the minutiae from a fingerprintimage has: a means for extracting the sweat pore feature from the valleyline and the sweat pore line; a means for analyzing the sweat porefeature of the several valley lines in the direction orthogonal to theridge and judging the pore to be the true sweat pore if the sweat porefeature pattern agrees with a preset condition; a means for extractingthe sweat pore area by using the ratio of the number of the pixelsjudged to be a true sweat pore; and a means for correcting the pixeldensity within the sweat pore area.

(3) The configuration for extracting the minutiae from a fingerprintimage has: a means for extracting the incipient ridge feature from theridge; a means for analyzing the incipient ridge feature of severalridges in the direction orthogonal to the ridge and judging the ridge tobe the true ridge if the incipient ridge feature pattern agrees with apreset condition; a means for extracting the ridge area by using theratio of the number of the pixels judged to be a true ridge; and a meansfor emphasizing the pixel density within the ridge area.

(4) The configuration for extracting the minutiae from a fingerprintimage has: a means for extracting the sweat pore feature from the valleyline; a means for analyzing the sweat pore feature of the several valleylines in the direction orthogonal to the ridge and judging the valley tobe the true valley line if the sweat pore feature pattern agrees with apreset condition; a means for extracting the valley line area by usingthe ratio of the number of the pixels judged to be a true valley line;and a means for emphasizing the pixel density within the valley linearea.

(5) The ridge width is employed as the incipient ridge feature in theitems (1) and (3).

(6) The ridge direction density is employed as the incipient ridgefeature in the items (1) and (3).

(7) The ridge direction density is employed as the sweat pore feature inthe items (2) and (4).

(8) The density difference accumulated value is employed as the sweatpore feature in the items (2) and (4).

(9) The white pixel radius is employed as the sweat pore feature in theitems (2) and (4).

As mentioned above, according to the embodiment of the presentinvention, the system for improving the minutiae extraction accuracyhaving the configuration which is characterized by including; the meansfor accurately extracting and removing the incipient ridge line and thesweat pore, even for the fingerprint image that is difficult to identifythe ridge because of the influences of the incipient ridge lines and thesweat pores, in the feature extracting function for the fingerprintimage; and the means for extracting and emphasizing the ridge lines andthe valley lines is provided.

When the fingerprint image having the thick incipient ridge lines isobserved in the direction orthogonal to the ridge, the incipient ridgelines and the true ridge lines appear alternately. According to theembodiment of the present invention, a configuration is provided, bywhich the incipient ridge lines are accurately extracted and removed byusing the method which extracts the incipient ridge feature, analyzesits appearance pattern and judges it in accordance with a presetcondition.

Similarly, when the fingerprint image having the thick sweat pores isobserved in the direction orthogonal to the ridge, sweat pore lines andtrue valley lines appear alternately. According to the embodiment of thepresent invention, a configuration is provided, by which the sweat poresare accurately extracted and removed by using the method which extractsthe sweat pore feature, analyzes its appearance pattern and judges it inaccordance with the preset condition.

Also according to the embodiment of the present invention, aconfiguration is provided, by which the true ridge lines can beaccurately extracted by using the method that for the fingerprint imagewithout any thick incipient ridge line, the incipient ridge feature isextracted, the incipient ridge features of the several ridge lines areanalyzed in the direction orthogonal to the ridge line and thenonexistence of the significant difference between all of the incipientridge features is judged in accordance with a preset condition.

Moreover, according to the embodiment of the present invention, aconfiguration is provided, by which the true valley lines can beaccurately extracted by using the method that for the fingerprint imagewithout any thick sweat pore, the pore feature is extracted, the porefeatures of the several valley lines are analyzed in the directionorthogonal to the ridge line and the nonexistence of the significantdifference between all of the pore features is judged in accordance witha preset condition.

1. A system for recognizing a fingerprint ridge, the system comprising:a ridge direction extracting unit configured to extract a longitudinaldirection of a ridge from said fingerprint image; a false featureanalyzing unit configured to classify a plurality of valleys into truevalleys and pores based on an analysis of a pore feature pattern of saidplurality of valleys in a direction crossing to said longitudinaldirection; a pore density correcting unit configured to generate acorrected image data by approximating densities of pixels classifiedinto said pores close to a density of a ridge of said fingerprint image;a true valley area extracting unit configured to extract a true valleyarea in which a rate of pixels belonging to said true valley is higherthan a predetermined condition as a true valley area; and a true valleyaccentuating unit configured to correct a density of a pixel belongingto said true valley area to a density of a pixel that is in aneighboring area and has a higher density, wherein said pore featurepattern includes an average value of densities of nearby pixels arrangedin said longitudinal direction, an accumulated value of differencesbetween adjacent pixels, or a white pixel radius being a maximum radiusof a circle including only pixels belonging to said true valleys andcentered at a focused pixel located in said plurality of valleys.
 2. Thesystem according to claim 1, wherein said pore feature pattern includesan average value of densities of nearby pixels arranged in saidlongitudinal direction.
 3. The system according to claim 1, wherein saidpore feature pattern includes an accumulated value of differencesbetween adjacent pixels.
 4. The system according to claim 1, furthercomprising: a binary image extracting unit configured to extract abinary image wherein pixels of said fingerprint image are classifiedinto ridge pixels and valley pixels, wherein said false featureanalyzing unit is configured to classify a focused pixel located in saidplurality of valleys into said pore based on a maximum radius of acircle centered at said focused pixel and including only said valleypixels.
 5. A method for recognizing a fingerprint ridge, the methodcomprising: extracting a longitudinal direction of a ridge from afingerprint image; classifying a plurality of valleys into true valleysand pores based on an analysis of a pore feature pattern of saidplurality of valleys in a direction crossing to said longitudinaldirection; generating a corrected image data by approximating densitiesof pixels classified into said pores close to a density of a ridge ofsaid fingerprint image; extracting a true valley area in which a rate ofpixels belonging to said true valley is higher than a predeterminedcondition as a true valley area; and correcting a density of a pixelbelonging to said true valley area to a density of a pixel that is in aneighboring area and has a higher density.
 6. The method according toclaim 5, further comprising: extracting an area extending in saidlongitudinal direction in which an occupancy rate of said true valley ishigher than a predetermined condition as a valley area; and accentuatingsaid valley area.
 7. A non-transitory computer readable medium softwareproduct for executing a method for recognizing a fingerprint ridge, themethod comprising: extracting a longitudinal direction of a ridge from afingerprint image; classifying a plurality of valleys into true valleysand pores based on an analysis of a pattern of densities of saidplurality of valleys in a direction crossing to said longitudinaldirection; generating a corrected image data by approximating densitiesof pixels judged as said pores close to a density of a ridge of saidfingerprint image; extracting a true valley area in which a rate ofpixels belonging to said true valley is higher than a predeterminedcondition as a true valley area; and correcting a density of a pixelbelonging to said true valley area to a density of a pixel that is in aneighboring area and has a higher density.
 8. The non-transitorycomputer readable medium software product according to claim 7, themethod further comprising: extracting an area extending in saidlongitudinal direction in which an occupancy rate of said true valley ishigher than a predetermined condition as a valley area; and accentuatingsaid valley area.